LexIssue: Benchmarking Legal Issue Identification in Chinese Civil Litigation

Abstract:Identifying the issues disputed between litigating parties is a crucial component of real-world litigation. However, legal issues remain comparatively underexplored in legal AI research. In this work, we study the computational modelling of legal issue identification in litigation. We introduce a legally grounded hierarchical schema that represents legal issues through both free-form issue descriptions and structured legal categories, and formulate legal issue identification as two complementary tasks: legal issue generation and legal issue classification. Based on this formulation, we construct LexIssue, a benchmark containing 430 real-world Chinese civil litigation cases and 1,303 expert-annotated disputed legal issues. We further develop an issue-centric legal knowledge base spanning 27 causes of action and 441 candidate legal issue entries to support retrieval-augmented reasoning. Experimental results across a diverse set of models show that retrieval-augmented generation using the constructed legal issue knowledge base consistently improves performance in identifying disputed legal issues and their corresponding legal attributes.

Computation and Language (cs.CL)

arXiv:2609.02954 [cs.CL]

(or arXiv:2609.02954v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.02954 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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